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Machine Learning

Machine learning in finance: history, technologies and outlook

In its analysis of over 1,400 use cases from “Eye on Innovation” in Financial Services Awards, Gartner found that machine learning (ML) is the top technology used to empower innovations at financial services firms, with operational efficiency and cost optimisation as key intended business outcomes. ML is a branch of artificial intelligence (AI) that involves the development of algorithms and models capable of automatically learning and improving from data.

Monitoring machine learning models in production with Grafana and ClearML

Victor Sonck is a Developer Advocate for ClearML, an open source platform for Machine Learning Operations (MLOps). MLOps platforms facilitate the deployment and management of machine learning models in production. As most machine learning engineers can attest, ML model serving in production is hard. But one way to make it easier is to connect your model serving engine with the rest of your MLOps stack, and then use Grafana to monitor model predictions and speed.

More modern monitoring: how telemetry and machine learning revolutionize system monitoring

It’s time, take your things and let’s move on to more modern monitoring. Relax, I know how difficult the changes are for you, but if you were able to accept the arrival of DTT and the euro, you sure got this! But first let us do a little review: Traditional system monitoring solutions rely on polling different meters, such as the Simple Network Management Protocol (SNMP), to retrieve data and react to it.

ML-Powered Assistance for Adaptive Thresholding in ITSI

Adaptive thresholding in Splunk IT Service Intelligence (ITSI) is a useful capability for key performance indicator (KPI) monitoring. It allows thresholds to be updated at a regular interval depending on how the values of KPIs change over time. Adaptive thresholding has many parameters through which users can customize its behavior, including time policies, algorithms and thresholds.

Sponsored Post

Operationalizing AI: MLOps, DataOps And AIOps

Originally posted on Forbes Technology Council As organizations increasingly embark on their digital transformation journey, IT is turning into a profit center, rather than a cost center. CIOs (chief information officers) are more than often referred to as chief innovation officers. New roles like chief data officer and chief analytics officer are rising to prominence. AI and data are at the center of this transformation, as CxOs are faced with daunting challenges in.

The Leading MLOps Tools

MLOps stands for Machine Learning Operations. MLOps refers to the set of practices and tools that facilitate the end-to-end lifecycle management of machine learning models, from development and training to deployment and monitoring. The primary objective of MLOps tools is to address the unique challenges associated with deploying and managing machine learning models in real-world scenarios.

Hacking our Way Towards ML-first Jupyter Notebooks - Civo Navigate NA 2023

In this Navigate 2023 talk, Matt Dupree discusses various challenges in data work and machine learning and proposes potential solutions. He highlights the importance of static analysis to address the problem of refactoring in data notebooks. Dupree emphasizes the need for automated testing workflows using IPython's hooks and profiles to minimize errors and missed opportunities. Furthermore, he suggests the development of Jupyter Bridge plugins to alleviate the repetitive typing of code and simplify the interaction between Python and JavaScript.